Results 171 to 180 of about 350,996 (311)

SVM-Maj: a majorization approach to linear support vector machines with different hinge errors

Support vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual.
Groenen, P.J.F., Bioch, J.C., Nalbantov, G.I.   +2 more
core  

Support-vector-machine based automatic performance modelling and optimisation for analogue and mixed-signal designs

, 2008
The growing popularity of analogue and mixed-signal (AMS) ASIC and SoC designs for communication applications has led to an increasing requirement for high efficiency performance modelling and optimisation methodologies in AMS synthesis systems. Recently,
Ren, Xianqiang
core  

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Correction: Retrieving interpretability to support vector machine regression models in dynamic system identification. [PDF]

Front Artif Intell
Pena-Campos J, Patino D, Ocampo-Martinez C, Ramos-Fernández JC, Salas-Brown M, Caicedo A.   +5 more
europepmc   +1 more source

Classification with support hyperplanes

A new classification method is proposed, called Support Hy-perplanes (SHs). To solve the binary classification task, SHs consider theset of all hyperplanes that do not make classification mistakes, referredto as semi-consistent hyperplanes. A test object
Groenen, P.J.F., Bioch, J.C., Nalbantov, G.I.   +2 more
core  

On robustness properties of convex risk minimization methods for pattern recognition [PDF]

The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness properties of machine learning methods based on convex risk minimization are investigated for the problem of pattern recognition ...
Christmann, Andreas, Steinwart, Ingo
core  

Topology and Material Optimization in Ultra‐Soft Magneto‐Active Structures: Making Advantage of Residual Anisotropies

Advanced Materials, EarlyView.
Residual magnetization induces pronounced mechanical anisotropy in ultra‐soft magnetorheological elastomers, shaping deformation and actuation even without external magnetic fields. This study introduces a computational‐experimental framework integrating magneto‐mechanical coupling into topology optimization for designing soft magnetic actuators with ...
Carlos Perez‐Garcia, Rogelio Ortigosa, Jesús Martínez‐Frutos, Daniel Garcia‐Gonzalez   +3 more
wiley   +1 more source

Comprehensive evaluation of gas-bearing properties in ultra-deep basement reservoirs based on an optimizable support vector machine. [PDF]

Sci Rep
Huang X, Tang J, Zhao J, Li J, Yin S, Feng S, Li H.   +6 more
europepmc   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

IR Spectroscopy and Linear Support Vector Machine Analysis of Colorectal Liver Metastasis. [PDF]

J Phys Chem B
Coe JV, Allen HC, Skarda SA, Orr TD, Wilson Ii AL, Wilson DG, Kalliantas S, Nystrom SV, Bradley RC, Li R, Chen Z, Hitchcock CL, Martin EW.   +12 more
europepmc   +1 more source